library(sf) #for polygons
library(tidyverse) #for data wrangling
library(tmap) #for interactive maps
library(janitor) #for cleaning names
#create list of years
kern_field_years <- c(2016:2019)
#loop through read-in process
for (i in kern_field_years) {
filename <- paste0("kernfields_", i)
wd <- paste0("data/kernfields/kern", i, ".shp")
assign(filename, read_sf(wd))
}
#select region (based off of farmer)
kern_subset <- kernfields_2019 %>%
filter(AGENT == "McMANUS/WILSON,MICHELE/AARIN")
#crop several other years
kern_2017_crop <- st_crop(kernfields_2017, kern_subset)
kern_2018_crop <- st_crop(kernfields_2018, kern_subset)
kern_2019_crop <- st_crop(kernfields_2019, kern_subset)
#create list of cropped fields and tidy
kern_cropped_list <- lapply(mget(sprintf("kern_%d_crop", 2017:2019)),
function(x)
{
clean_names(x) %>%
st_make_valid()
}
)
#bind cropped data together, include new column "source" saying what dataset it came from
sample_bind <- do.call(rbind, lapply(names(kern_cropped_list),
function(x)
cbind(kern_cropped_list[[x]],
source = x)
)) %>%
#turn source column into year column by keeping only numbers
mutate(year = gsub("\\D", "", source)) %>%
#Create ID column
mutate(id = row_number()) %>%
#merge ID column to pmt_site
mutate(pmt_site_id = str_c(pmt_site, "-", id)) %>%
#merge this column with year
mutate(pmt_site_string = str_c(year, "_", pmt_site_id))
#select only column we need, clean geometry
sample_clean <- sample_bind %>%
dplyr::select(pmt_site_string) %>%
st_make_valid()
#write to shapefile
#st_write(sample_clean, "data/shapefiles_written/sample_clean.shp", append = F)
I took the shapefile sample_clean.shp and uploaded it to mapshaper’s online interface (https://mapshaper.org/). Then I entered in the console $ mosaic and downloaded results as a shapefile, as seen in the next code chunk.
This thread could potentially improve workflow https://github.com/mbloch/mapshaper/issues/353
#read in mapshaper output
sample_mosiac <- st_read("data/mapshaper_outputs/sample_clean/sample_clean.shp")
## Reading layer `sample_clean' from data source
## `/Users/irisfoxfoot/Desktop/Kern RA/kernRA_coding_projects/data/mapshaper_outputs/sample_clean/sample_clean.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 817 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 6159540 ymin: 2352351 xmax: 6187962 ymax: 2386672
## Projected CRS: NAD83 / California zone 5 (ftUS)
#intersect with field data
sample_join <- st_intersection(st_buffer(sample_mosiac, dist = -1), sample_clean)
#pivot so each column is a year
sample_pivot <- sample_join %>%
separate(pmt_site_string, into = c("year", "pmt_site"), sep = "_") %>%
select(-FID) %>%
pivot_wider(names_from = year, values_from = pmt_site) %>%
st_sf() %>%
st_buffer(dist = 1) %>%
mutate(area_sq_ft = st_area(.))
#unnest listed pmt_site so they are each in own column
sample_pivot_sep <- sample_pivot %>%
unnest_wider(c(`2017`, `2018`, `2019`), names_sep = "_")
#write to csv
#write_csv(sample_pivot_sep, "data/shapefiles_written/2017_2019_sample.csv")
#write to shp file
#st_write(sample_pivot_sep, "data/shapefiles_written/2017_2019_shapefile.shp")
pmt_deleted <- function (pivot_sep_name) {
#create df of pivot table without geometry
pivot_sep_no_geo <- pivot_sep_name %>%
select(-geometry, -area_sq_ft)
#create function to check if pmt_site is present anywhere in the pivot table
pmt_present <- function(x)
{
present <- any(pivot_sep_no_geo==x)
return(present)
}
#create list of original pmt_sites
pmt_list <- as.list(sample_bind$pmt_site_id)
#run through list and check if they are in pivot df
present_list <- lapply(pmt_list, pmt_present)
#create df of results
present_df <- as.data.frame(cbind(pmt_list, present_list)) %>%
filter(is.na(present_list))
return(present_df)
}
#get rid of small fields
sample_pivot_over_acre <- sample_pivot %>%
filter(area_sq_ft >= units::set_units(43560,"ft^2"))
#unnest listed pmt_site so they are each in own column
sample_pivot_over_acre_sep <- sample_pivot_over_acre %>%
unnest_wider(c(`2017`, `2018`, `2019`), names_sep = "_")
#what fields are under one acre?
under_acre <- sample_bind %>%
st_sf() %>%
mutate(area = st_area(.)) %>%
filter(area < units::set_units(43560,"ft^2"))
#gets rid of many but not all slivers
#deletes 65 true fields but, there are only 60 true fields that are under one acre.
#what are the other five?
#intersect with field data BUT negative buffer field data
neg_sample_join<- st_intersection(st_buffer(sample_mosiac, dist = -20), sample_clean)
#pivot so each column is a year
neg_sample_pivot <- neg_sample_join %>%
separate(pmt_site_string, into = c("year", "pmt_site"), sep = "_") %>%
select(-FID) %>%
pivot_wider(names_from = year, values_from = pmt_site) %>%
st_sf() %>%
filter(!st_is_empty(.)) %>%
st_buffer(dist = 1) %>%
mutate(area_sq_ft = st_area(.))
#unnest listed pmt_site so they are each in own column
neg_sample_pivot_sep <- neg_sample_pivot %>%
unnest_wider(c(`2017`, `2018`, `2019`), names_sep = "_")
#is every permit_site represented in the pivot table?
#with a buffer of -10 we miss 16 fields
#with a buffer of -20 we miss 31 fields
#with a buffer of -30 we miss 40 fields
#with a buffer of -50 we miss 76 fields
#create column indicating if it was duplicated, area column
sample_conditional <- sample_join %>%
group_by(pmt_site_string) %>%
mutate(dup = n()>1) %>%
ungroup() %>%
mutate(area_sq_ft = st_area(.))
#filter so fields smaller than one acre are only dropped if duplicated
sample_conditional_drop <- sample_conditional %>%
filter(!(dup == "TRUE" & area_sq_ft <= units::set_units(43560,"ft^2")))
#pivot so each column is a year
conditional_sample_pivot <- sample_conditional_drop %>%
separate(pmt_site_string, into = c("year", "pmt_site"), sep = "_") %>%
select(-FID) %>%
pivot_wider(names_from = year, values_from = pmt_site) %>%
st_sf() %>%
filter(!st_is_empty(.)) %>%
st_buffer(dist = 1) %>%
mutate(area_sq_ft = st_area(.))
#unnest listed pmt_site so they are each in own column
conditional_sample_pivot_sep <- conditional_sample_pivot %>%
unnest_wider(c(`2017`, `2018`, `2019`), names_sep = "_")
#drops 25
#reference what output should be
tmap_mode("view")
tm_shape(sample_bind) +
tm_polygons(id = "pmt_site") +
tm_facets(by = "year", sync = T) +
tm_layout(panel.labels = c("2017", "2018", "2019"))
#test output
tmap_mode("view")
tm_shape(sample_pivot) +
tm_polygons(id = "pmt_site_year") +
tmap_options(check.and.fix = TRUE)
#test output
tmap_mode("view")
tm_shape(sample_pivot_over_acre) +
tm_polygons(id = "pmt_site_year") +
tmap_options(check.and.fix = TRUE)
#test output
tmap_mode("view")
tm_shape(conditional_sample_pivot) +
tm_polygons(id = "pmt_site_year") +
tmap_options(check.and.fix = TRUE)
#test output
tmap_mode("view")
tm_shape(neg_sample_pivot) +
tm_polygons(id = "pmt_site_year") +
tmap_options(check.and.fix = TRUE)